Book Image

Extending Power BI with Python and R

By : Luca Zavarella
Book Image

Extending Power BI with Python and R

By: Luca Zavarella

Overview of this book

Python and R allow you to extend Power BI capabilities to simplify ingestion and transformation activities, enhance dashboards, and highlight insights. With this book, you'll be able to make your artifacts far more interesting and rich in insights using analytical languages. You'll start by learning how to configure your Power BI environment to use your Python and R scripts. The book then explores data ingestion and data transformation extensions, and advances to focus on data augmentation and data visualization. You'll understand how to import data from external sources and transform them using complex algorithms. The book helps you implement personal data de-identification methods such as pseudonymization, anonymization, and masking in Power BI. You'll be able to call external APIs to enrich your data much more quickly using Python programming and R programming. Later, you'll learn advanced Python and R techniques to perform in-depth analysis and extract valuable information using statistics and machine learning. You'll also understand the main statistical features of datasets by plotting multiple visual graphs in the process of creating a machine learning model. By the end of this book, you’ll be able to enrich your Power BI data models and visualizations using complex algorithms in Python and R.
Table of Contents (22 chapters)
1
Section 1: Best Practices for Using R and Python in Power BI
5
Section 2: Data Ingestion and Transformation with R and Python in Power BI
11
Section 3: Data Enrichment with R and Python in Power BI
17
Section 3: Data Visualization with R in Power BI

Importing RDS files in R

In this section, you will develop mainly R code, and in the various examples, we will give you an overview of what we are going to do. If you have little experience with R, you should familiarize yourself with the data structures that R provides by starting with this quickstart: http://bit.ly/r-data-struct-quickstart. Take a look at the References section for more in-depth information.

A brief introduction to Tidyverse

A data scientist using R as an analytical language for data analysis and data science must know the set of packages that goes by the name of Tidyverse (https://www.tidyverse.org). It provides everything needed for data wrangling and data visualization, giving the analyst a consistent approach to the entire ecosystem of packages it provides. In this way, it tries to heal the initial situation of "chaos" of R functionalities provided by packages developed by developers who had not agreed on a common framework.

Note

If you...